code: def predict(
      self, states, actions, rewards, returns_to_go, timesteps, **kwargs
  ):
      action = self.alg.predict(
          states, actions, rewards, returns_to_go, timesteps
      )
      actions[-1] = action
      action = action.detach().cpu().numpy()
      return action
  
file_path: PARL/benchmark/torch/DT/agent.py
imports: import parl
  import torch
  import numpy
  from data_loader import DataLoader
label_desc: Use image node_id 2 for calling the DTAgent obj's underlying member method code with example usage: obj.predict(states, actions, rewards, returns_to_go, timesteps) and returns: action